Around the globe, large contiguous forests are being broken up into smaller patches by newly constructed roads and fields flattened for agriculture. Today, 20% of all forests are located within 100 meters of a non-forest edge. Compared to forest interiors, trees near edges receive more wind and sunlight exposure as well as altered nutrient deposition. However, common biomass models based on forest interior data do not account for these conditions. In 2015, studies found that carbon stock models overestimated the biomass of global tropical forests by 10% because they did not account for biomass reductions near forest edges. While research on tropical forests has generally found lower biomass near edges, the impact of edges on temperate broadleaf forests is still being debated. Studies using satellite data to estimate biomass have pointed to a decrease in biomass near temperate forest edges, while field studies indicated the opposite trend. The goal of my study is to clarify the impact of edge conditions on temperate forest biomass and investigate how biomass estimates based on remote sensing can fail to account for edge effects. I measured trees at 63 forest edge plots located across central Massachusetts and found that, on average, biomass density at edges was significantly higher than forest interiors. I also developed a machine learning model that estimates forest biomass based on airborne LiDAR measurements of canopy height and trained it on forest interior data. When tested on plots at forest edges, the model underestimated biomass by around 10%. These findings reveal a biomass enhancement at forest edges that can be missed by models reliant on remote sensing and calibrated with forest interior data. As such, this study plays a key role in informing future research on temperate forest edge effects and developments towards improved, edge-aware forest models for carbon capacity calculations.